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Siti Maesaroh
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Journal Collabits
Published by Universitas Mercu Buana
ISSN : 30628601     EISSN : 30466709     DOI : http://dx.doi.org/10.22441/collabits
Core Subject : Science, Engineering,
Computer Science & IT Engineering
Journal Collabits adalah jurnal yang membahas strategi keamanan cyber untuk meningkatkan kinerja dan keandalan dalam implementasi teknologi kecerdasan buatan (AI), kecerdasan bisnis (BI), dan sains data, yang di kelola oleh Fakultas Ilmu Komputer (FASILKOM) terdiri dari dua prodi yaitu Teknik Informatika (TI dan Prodi Sistem Informasi (SI). Dengan pertumbuhan pesat dalam penggunaan teknologi ini, keamanan cyber menjadi semakin penting dalam menjaga integritas, kerahasiaan, dan ketersediaan data. Tulisan ini mengeksplorasi berbagai pendekatan, alat, dan praktik terbaik dalam mengamankan sistem AI, BI, dan sains data, termasuk deteksi ancaman, enkripsi data, manajemen akses, dan pemulihan bencana. Jurnal ini juga menganalisis dampak kebijakan keamanan cyber pada inovasi teknologi dan memberikan rekomendasi untuk meningkatkan keamanan dalam ekosistem digital yang terus berkembang
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 10 Documents
 1 
Search results for , issue "vol. 3 no. 2 (2026)" : 10 Documents clear
Using Tensorflow for Clean and Messy Room Image Classification with Python Gilas Adi Saputra; Damar Pratama Ristadias Hariyanto; Roy Mubarak
Journal Collabits Vol. 3 No. 2 (2026)
Publisher : Journal Collabits

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/collabits.v3i2.27274

Abstract

Image classification is a fundamental computer vision task that can support automated visual monitoring in domestic, educational, and workplace environments. This study develops a transparent baseline pipeline using TensorFlow 2.x, Keras, and Python to distinguish clean and messy room images. The dataset contains 192 training images, with 96 images in each class, and 20 validation images, with 10 images in each class. All images are resized to 150 x 150 pixels and normalized to a 0-1 range. Rotation, horizontal flipping, and shearing are applied only to the training data, while validation images are normalized without random transformation. The sequential convolutional neural network contains four convolution-pooling blocks, a fully connected layer, and a sigmoid output for binary classification. Qualitative testing with two external images produced labels that were consistent with visual observation: the cluttered room was classified as messy and the organized room as clean. These demonstrations confirm that the pipeline operates from image input to class prediction, but they do not establish broad generalization or perfect accuracy. The main contribution is a reproducible small-data workflow that documents dataset distribution, preprocessing, augmentation, model parameters, validation procedures, and prediction thresholds. The study is limited by the small validation set, the absence of a large independent test set, and the lack of direct comparison with pretrained models. Future studies should evaluate transfer learning, larger datasets, repeated trials, and metrics such as precision, recall, F1-score, and confusion matrices.
Improving the Quality of Decision Making in Educational Institutions with Business Intelligence Diar Eka Purnama; Abdul Hamid Arribathi; Nur Azizah; Henderi Henderi
Journal Collabits Vol. 3 No. 2 (2026)
Publisher : Journal Collabits

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/collabits.v3i2.39388

Abstract

Effective decision-making in educational institutions relies heavily on the availability of accurate, timely, and easy-to-interpret data. However, many schools and colleges still face challenges in managing data spread across various systems, so decisions are often made based on assumptions or partial information. This study aims to analyze the use of Business Intelligence (BI) to improve the quality of decision-making in educational institutions. In this study, a qualitative approach is used with case studies in three educational institutions that have implemented the BI system. The procedure in this study is by: collecting data through interviews with institutional leaders, observation of the use of analytical dashboards, and analysis of policy documents. The results show that BI enables real-time data visualization related to academic performance, resource allocation, and stakeholder satisfaction, thereby accelerating and strengthening strategic and operational decision-making processes. In addition, BI encourages a data-driven culture that increases transparency and accountability. However, the success of BI's implementation is greatly influenced by the readiness of digital infrastructure, human resource competence, and the commitment of top management. This study concludes that BI is an effective strategic tool for education governance transformation, as long as it is holistically integrated into the institutional management system. These findings provide practical recommendations for education leaders seeking to improve the quality of decisions through the use of analytics technology.
Web-Based Multi-Cafe Information System in the West Jakarta Area Luthfia Fauzia Dewi Ariyanti; Janu Ilham Saputro; Eduard Hotman Purba; Muhammad Nur Habibie
Journal Collabits Vol. 3 No. 2 (2026)
Publisher : Journal Collabits

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/collabits.v3i2.39597

Abstract

Evolving urban lifestyles have driven increased public interest—particularly in West Jakarta—in visiting cafes for relaxation, work, and socializing. However, information regarding cafe locations, menus, prices, facilities, and operating hours remains fragmented and difficult to access centrally, making it challenging for potential customers to make informed choices. This research aims to design and develop a web-based information system that integrates data from various cafes in West Jakarta into a single, unified platform. The system development follows the Waterfall model, encompassing requirements analysis, system design, implementation, testing, and maintenance. The system was built using web-based programming languages and a relational database to store cafe information, incorporating features such as search functionality, location- and category-based filtering, and user reviews. System testing utilized the Black Box Testing method to ensure all functions operated according to requirements, alongside usability testing to assess the system's ease of use for end-users. The results indicate that this multi-cafe information system enables users to quickly and accurately search for and compare cafe information while simultaneously assisting cafe owners in digitally promoting their businesses. This system is expected to serve as a beneficial, unified information solution for both the public and cafe business owners in West Jakarta.
Design and Implementation of a Python-Based Cinema Ticket Ordering Prototype Using Linear Search Ramando Girsang; Hisyam Fadhila; Yunita Sartika sari; Nia Rahmah Kurnianda; Ifan Prihandi
Journal Collabits Vol. 3 No. 2 (2026)
Publisher : Journal Collabits

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/collabits.v3i2.27260

Abstract

The increasing adoption of digital services has encouraged the development of ticketing systems that simplify access to movie information and transaction processes. This study aims to design and implement a Python-based cinema ticket ordering prototype and to examine the suitability of the Linear Search algorithm for movie-title lookup in a small and unsorted dataset. The development process consists of requirements analysis, system design, implementation, functional verification, and algorithmic complexity analysis. The prototype supports customer input, movie selection, ticket quantity calculation, optional food and beverage ordering, and transaction calculation. Linear Search is selected because it can operate directly on a small unsorted list without preprocessing or an additional indexing structure. Theoretical comparison shows that Linear Search has O(n) average and worst-case time complexity, while Binary Search offers O(log n) complexity for sorted data and hash-based lookup offers O(1) average lookup time with additional storage requirements. The study therefore does not claim Linear Search as a new or universally efficient algorithm. Its contribution lies in presenting a transparent small-scale ticket-ordering architecture and an academically justified algorithm selection. Future development should incorporate persistent database storage, a graphical or web/mobile interface, authentication, secure payment integration, usability evaluation, and empirical performance benchmarking.
Analysis of Best-Selling Product Sales at Hatfina Hijab Using Association Rule Mining for Business Intelligence Khairunnisa Ramadhan; Siti Maesaroh; Nadia Kayla Dhinita
Journal Collabits Vol. 3 No. 2 (2026)
Publisher : Journal Collabits

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/collabits.v3i2.27287

Abstract

This study analyzes customer purchasing patterns at Hatfina Hijab and translates the resulting association rules into actionable Business Intelligence (BI) recommendations. The dataset consists of 174 sales transactions recorded from February to April 2024. After data cleaning, product items were transformed into a binary transaction matrix and processed using association rule mining in RapidMiner. The baseline rule set was obtained using a minimum support of 0.30 and a minimum confidence of 0.50. The revised analysis complements the original confidence values with support and lift to distinguish rules that are frequent from those that represent genuinely positive product associations. Nineteen baseline rules were reported, of which sixteen have lift values greater than 1.00 and therefore indicate positive associations. The strongest confidence was found in the rule Hampers Paket Hemat 2 Hijab and Hampers Paket Hemat 1 Hijab -> Souvenir Sajadah (confidence = 1.000; lift = 1.475). Parameter sensitivity analysis shows that stricter support-confidence thresholds reduce the retained rule set from 19 rules at 0.30/0.50 to 14 rules at 0.35/0.60, 13 rules at 0.40/0.70, 3 rules at 0.45/0.80, and 1 rule at 0.50/0.90. The findings are interpreted into recommendations for product bundling, cross-selling, promotional design, and coordinated inventory planning. The study demonstrates how association rule mining can function as an analytical component of BI and Decision Support Systems (DSS), while emphasizing that association patterns indicate co-occurrence rather than causality.
Online Food Ordering App Rafif Syari Hidayah; Rehan Saputra; Krisna Septiawan; Muhammad Aditya Ramadhan; Misni Misni
Journal Collabits Vol. 3 No. 2 (2026)
Publisher : Journal Collabits

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/collabits.v3i2.31236

Abstract

Online food ordering systems have become an important component of digital commerce because users increasingly expect fast, convenient, and reliable transaction processes. However, system development should not focus only on functional ordering features; it should also consider usability, transaction security, and the ability to transform transaction records into information for future analytical use. This study aims to design and develop a web-based Online Food Ordering Application using the Software Development Life Cycle (SDLC) Waterfall approach. User and seller requirements are translated into major functions including registration and authentication, food and beverage browsing, shopping cart management, checkout, payment, order history, product management, and order confirmation. A conceptual framework is introduced to explain the relationships among user needs, system design, usability, security, transaction efficiency, and the analytical utilization of transaction data. The application was implemented using PHP with HTML and CSS for the interface and a database for operational records. Functional validation was conducted using black-box testing on the documented login, menu, and payment scenarios. All documented scenarios produced the expected outputs and were categorized as passed. The results show that the prototype supports the basic food ordering workflow. Nevertheless, the present evaluation is limited to functional testing and does not yet provide quantitative usability measurements, comprehensive security testing, transaction-time benchmarking, or experimental evaluation of recommender and Business Intelligence modules. The stored transaction and order-history data are therefore positioned as a foundation for future behavioral analytics, Business Intelligence dashboards, and personalized food recommendation development. TRANSLATE with x English Arabic Hebrew Polish Bulgarian Hindi Portuguese Catalan Hmong Daw Romanian Chinese Simplified Hungarian Russian Chinese Traditional Indonesian Slovak Czech Italian Slovenian Danish Japanese Spanish Dutch Klingon Swedish English Korean Thai Estonian Latvian Turkish Finnish Lithuanian Ukrainian French Malay Urdu German Maltese Vietnamese Greek Norwegian Welsh Haitian Creole Persian     TRANSLATE with COPY THE URL BELOW Back EMBED THE SNIPPET BELOW IN YOUR SITE Enable collaborative features and customize widget: Bing Webmaster Portal Back
Public Sentiment Analysis of the Rupiah Redenomination Policy Discourse Using IndoBERT on Social Media Data Azkar Rindra Maulana; Aliffian Alfarish; Gibran Abdillah
Journal Collabits Vol. 3 No. 2 (2026)
Publisher : Journal Collabits

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Abstract

The discourse on rupiah redenomination has regained momentum in the 2025–2027 policy agenda, with the discussion of the draft regulation on “Rupiah Price Changes.” In legislative studies, redenomination is positioned as a measure to simplify nominal values in order to improve the efficiency of transactions, accounting, and financial system operations, one of which is through the removal of three zeros from the rupiah [1]. On the other hand, Bank Indonesia emphasizes that redenomination is different from sanering because it does not aim to reduce people's purchasing power, but rather to simplify nominal values in stable economic conditions [2]. It is important to emphasize this difference in concept because the public often equates the “removal of zeros” with a reduction in value, giving rise to concerns that are not always in line with the definition of the policy.
Data Cleaning and Exploratory Data Analysis for Heart Disease Prediction Foezi Arisandi SJ; Dendy Jonas Managas
Journal Collabits Vol. 3 No. 2 (2026)
Publisher : Journal Collabits

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/collabits.v3i2.37650

Abstract

Cardiovascular disorders remain a major cause of death worldwide, emphasizing the importance of accurate and timely diagnostic support. This study presents a data-driven framework for predicting heart disease by integrating structured data cleaning procedures, exploratory data analysis (EDA), and supervised machine learning classification. The Heart Statlog (Cleveland-Hungary) dataset, which contains a range of clinical attributes associated with cardiac conditions, is used as the primary data source. To ensure data quality, several preprocessing steps are applied, including treatment of missing values, elimination of duplicate records, correction of inconsistent entries, and transformation of categorical variables into numerical representations. EDA is subsequently employed to explore feature distributions and inter-variable relationships using statistical measures and visualization techniques. Logistic Regression and Random Forest algorithms are implemented to construct predictive models and assess classification performance. The experimental results indicate that chest pain category, ST-segment slope, exercise-induced angina, and maximum heart rate are among the most influential predictors of heart disease. Furthermore, the Random Forest classifier achieves higher overall performance than Logistic Regression, suggesting its stronger capability in modeling complex clinical patterns. These findings confirm that rigorous preprocessing combined with appropriate machine learning methods can significantly enhance predictive accuracy and support the development of reliable clinical decision-support systems.
Prediction of Depression Prevalence Using Random Forest Regression on Global Mental Health Data Jovansyah Lazuardi Adhan; Alief Linggar Syamsudin; Razan Muhammad Ihsan
Journal Collabits Vol. 3 No. 2 (2026)
Publisher : Journal Collabits

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Abstract

The primary objective of this research is to forecast the likelihood of depression by applying the Random Forest algorithm to data regarding individual characteristics. The methodology encompasses several essential phases, such as data preprocessing, feature selection, and model training, which are designed to enhance data integrity and modeling precision. Random Forest was chosen for its efficacy in managing high-dimensional datasets and its capacity to model intricate, non-linear correlations between variables. By generating multiple decision trees based on random subsets of data and features, the model effectively identifies diverse patterns associated with depression risk. Experimental outcomes indicate that the Random Forest model attains superior predictive performance, surpassing various traditional classification techniques. The model exhibits robust generalization abilities, offering dependable predictions for identifying at-risk individuals. These results imply that Random Forest is a viable and practical tool for mental health risk assessment, potentially aiding mental health professionals and policymakers in formulating early intervention strategies.
Analisis Aksesibilitas Spasial Pejalan Kaki Stasiun LRT Jakarta Menggunakan Metode Isochrone Bagas Indra Lesmana; Jericho Jericho; Mohamad Yusuf
Journal Collabits Vol. 3 No. 2 (2026)
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Abstract

Pedestrian accessibility around public transportation stations is a key factor in the success of Transit-Oriented Development (TOD) systems in increasing ridership. This study measured, mapped, and analyzed pedestrian catchment areas around Jakarta LRT station corridors, with a travel time of up to 120 minutes, using the Geographic Information System (GIS) QGIS using Network Analysis and Isochrone Mapping methods. Road network data was obtained from OpenStreetMap (OSM) via the QuickOSM plugin. Analysis parameters were configured with a constant walking speed of 5 km/h (1.39 m/s) and a 5-minute time interval. The results show significant spatial disparities between stations. Rasuna Said Station (5,005 km²) and Setiabudi Station (4,182 km²) have optimal accessibility thanks to their extensive and connected arterial road networks and surrounding areas. In contrast, North Boulevard Station (0.008 km²), Velodrome (0.015 km²), and Cikoko (0.034 km²) experience spatial entrapment of pedestrians. This limitation is caused by physical barriers such as toll roads, concrete dividers, and a lack of public crossing facilities. Physical interventions are needed, including constructing pedestrian bridges (JPOs) and updating micro-spatial data on the OSM platform.

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